TY - GEN
T1 - Recent Research Study on AI-based Crime Scene Evidence Detection
AU - Murugan, Thangavel
AU - Aldahmani, Fotoon Khaleifah Abdulla Mosabbas
AU - Almehrzi, Ghalya Salem Mohamed Alshuraiqi
AU - Alsereidi, Eiman Mohamed Salem Mohamed
AU - Aldahmani, Aaesha Abdulla Khalfan Ali
AU - Alahbabi, Eiman Mubarak Masoud
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Traditional crime scene evidence detection methods are time-consuming and prone to human error. In recent years, artificial intelligence (A I) has revolutionized crime scene investigation by improving the detection and analysis of evidence. This review paper examines recent studies on AIbased evidence detection from key crime scenes, such as weapons, footprints, and bloodstains. Researchers have used advanced AI algorithms to develop innovative techniques for accurately identifying and documenting these crucial pieces of evidence, which helps law enforcement agencies solve criminal cases more efficiently. The paper discusses various AI models and technologies for detecting weapons, footprints, and bloodstains at crime scenes, highlighting their strengths, limitations, and potential for future advancements. Overall, the paper emphasizes the importance of AI in enhancing crime scene investigation processes and advocates for further research and development in this rapidly evolving field to maximize its potential impact on criminal justice systems worldwide.
AB - Traditional crime scene evidence detection methods are time-consuming and prone to human error. In recent years, artificial intelligence (A I) has revolutionized crime scene investigation by improving the detection and analysis of evidence. This review paper examines recent studies on AIbased evidence detection from key crime scenes, such as weapons, footprints, and bloodstains. Researchers have used advanced AI algorithms to develop innovative techniques for accurately identifying and documenting these crucial pieces of evidence, which helps law enforcement agencies solve criminal cases more efficiently. The paper discusses various AI models and technologies for detecting weapons, footprints, and bloodstains at crime scenes, highlighting their strengths, limitations, and potential for future advancements. Overall, the paper emphasizes the importance of AI in enhancing crime scene investigation processes and advocates for further research and development in this rapidly evolving field to maximize its potential impact on criminal justice systems worldwide.
KW - artificial intelligence
KW - bloodstains
KW - crime scene
KW - evidence
KW - footprint
KW - weapon
UR - http://www.scopus.com/inward/record.url?scp=85216423659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85216423659&partnerID=8YFLogxK
U2 - 10.1109/CommNet63022.2024.10793266
DO - 10.1109/CommNet63022.2024.10793266
M3 - Conference contribution
AN - SCOPUS:85216423659
T3 - Proceedings - 7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024
BT - Proceedings - 7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024
A2 - El Bouanani, Faissal
A2 - Ayoub, Fouad
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024
Y2 - 4 December 2024 through 6 December 2024
ER -